How does moulage contribute to medical students’ perceived engagement in simulation? A mixed-methods pilot study

INTRODUCTION: Moulage is used frequently in simulation, with emerging evidence for its use in fields such as paramedicine, radiography and dermatology. It is argued that moulage adds to realism in simulation, although recent work highlighted the ambiguity of moulage practice in simulation. In the absence of knowledge, this study sought to explore the impact of highly authentic moulage on engagement in simulation. METHODS: We conducted a randomised mixed-methods study exploring undergraduate medical students’ perception of engagement in relation to the authenticity moulage. Participants were randomised to one of three groups: control (no moulage, narrative only), low authenticity (LowAuth) or high authenticity (HighAuth). Measures included self-report of engagement, the Immersion Scale Reporting Instrument (ISRI), omission of treatment actions, time-to-treat and self-report of authenticity. In combination with these objective measures, we utilised the Stimulated Recall (SR) technique to conduct interviews immediately following the simulation. RESULTS: A total of 33 medical students participated in the study. There was no statistically significant difference between groups on the overall ISRI score. There were statistically significant results between groups on the self-reported engagement measure, and on the treatment actions, time-to-treat measures and the rating of authenticity. Four primary themes ((1) the rules of simulation, (2) believability, (3) consistency of presentation, (4) personal knowledge ) were extracted from the interview analysis, with a further 9 subthemes identified ((1) awareness of simulating, (2) making sense of the context (3) hidden agendas, (4) between two places, (5) dismissing, (6) person centredness, (7) missing information (8) level of training (9) previous experiences). CONCLUSIONS: Students rate moulage authenticity highly in simulations. The use of high-authenticity moulage impacts on their prioritisation and task completion. Although the slower performance in the HighAuth group did not have impact on simulated treatment outcomes, highly authentic moulage may be a stronger predictor of performance. Highly authentic moulage is preferable on the basis of optimising learning conditions.